Your top 3 RNA-seq quantification and differential expression tools
RNA-sequencing is progressively replacing microarrays for the study of transcriptomes, and comparison of gene expression. One advantage of this technique is the ability to identify and quantify the expression of isoforms and unknown transcripts.
To help you perform your experiments in the best conditions, we are closing our series of surveys on RNA-sequencing by asking OMICtools members to choose their favorite quantification and differential expression tools.
RNA quantification and differential expression
While microarrays produce a numerical estimate of the relative expression of genes across the genome, RNA-sequencing experiments rely on read-count distributions. After mapping reads to a reference genome, the expression level for each gene or isoform are estimated and normalized, and finally differentially-expressed genes are identified using statistical methods.
Two main methods exist for differential gene expression analysis, parametric and non-parametric. They mainly differ by the type of statistical test used to approximate gene expression (Costa-Silva et al.). With the increasing popularity of RNA-sequencing, many software tools have been developed for quantification and analysis of differential expression.
To help you choose between the plethora of available tools, we asked OMICtools members to choose for their favorite RNA-seq quantification and differential expression analysis tools. Here is the top 3 of this survey.
Your most favorited tool: DESeq
You were 82% to choose DESeq as one of your favorite RNA-seq quantification tools.
DESeq (and its latest version DESeq2) is a method that integrates methodological advances with features to facilitate quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change. This tool estimates variance-mean dependence in count data and uses a model that follows the negative binomial distribution.
Second place for Limma
Originally developed for the analysis of microarray data, Limma is a powerful tool that now allows to perform both differential expression and differential splicing analyses of RNA-seq data.
Limma relies on the voom methodology to perform the majority of analysis methods for use on RNA-seq data, such as random effects modelling and gene set testing (Ritchie et al.).
Limma is run as an R/Bioconductor software package and is freely accessible here.
Third position for EdgeR
You were a lot to plebiscite EdgeR in our survey so we thought it deserved the 3rd place!
EdgeR (empirical analysis of differential gene expression in R) implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi likelihood tests to analyze differential expression of RNA-seq data.
This powerful tool allows to perform several analysis such as pairwise comparisons between two or more groups, estimation of dispersion, gene ontology and pathway analysis (KEGG and GO), gene set testing, alternative splicing or CRISPR-Cas9 and shRNA-seq screen analysis.
Costa-Silva et al. (2017). RNA-Seq differential expression analysis: An extended review and a software tool. Plos One.
Love et al. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology.
Ritchie et al. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research.
McCarthy et al. (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research.